Comorbidity Measures for Use with Administrative Data
Comorbidity Measures for Use with Administrative Data is a dataset published in Medical Care (1998). On theSindex it has a DataRank of 35.1, placing it in the top 0.2% of the data-sharing corpus. It has been cited 9,866 times, with 195 citing works in its 1-hop citation network. Its calibrated FAIR score is 59/100.
Abstract
ObjectivesThis study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets.MethodsThe study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death.ResultsA comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders.ConclusionsThe comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
1.4
From this paper's citation signal
Citation Network Contribution
33.8
From 195 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- A new method of classifying prognostic comorbidity in longitudinal studies: Development and validationJournal of Chronic Diseases198749,462 citationsDataRank 1.6
- Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative DataMedical Care200510,527 citationsDataRank 1.4
- Adapting a clinical comorbidity index for use with ICD-9-CM administrative databasesJournal of Clinical Epidemiology199210,501 citationsDataRank 1.4
- The MIMIC Code Repository: enabling reproducibility in critical care researchJournal of the American Medical Informatics Association2017454 citationsDataRank 13.7Top 15%
- Risks of Subsequent Hospitalization and Death in Patients with Kidney DiseaseClinical Journal of the American Society of Nephrology2012108 citationsDataRank 5.2
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 4% comes from its base citations and 96% from the citation network (195 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.